OpenRaiser/Intern-Atlas huggingface dataset:
Understanding the Research-Practice Gap in Visualization Design Guidelines
Venue: arXiv cs.HC 2026
Year: 2026

Although empirical research often underpins practical visualization guidelines, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the research-practice gap in visualization design guidelines through a mixed-methods approach. We collected 390 design guidelines from practitioner-facing sources and 235 empirical studies to quantitatively assess their alignment. To complement this analysis, we conducted surveys with 69 participants (33 practitioners, 36 researchers) and in-depth interviews with 20 experts to examine their experiences, perceptions, and challenges. Our findings reveal discrepancies: empirical evidence often contradicts or only partially supports widely used guidelines, and the two communities prioritize different attributes of design. Based on these insights, we derive a holistic guideline template (integrating Context, Approach, Problem, and Purpose) and discuss actionable strategies, such as a triadic knowledge model.
Why Softmax Attention Outperforms Linear Attention
Venue: arXiv cs.CL 2026
Year: 2026

Large transformer models have achieved state-of-the-art results in numerous natural language processing tasks. Among the pivotal components of the transformer architecture, the attention mechanism plays a crucial role in capturing token interactions within sequences through the utilization of softmax function. Conversely, linear attention presents a more computationally efficient alternative by approximating the softmax operation with linear complexity. However, it exhibits substantial performance degradation when compared to the traditional softmax attention mechanism. In this paper, we bridge the gap in our theoretical understanding of the reasons behind the practical performance gap between softmax and linear attention. By conducting a comprehensive comparative analysis of these two attention mechanisms, we shed light on the underlying reasons for why softmax attention outperforms linear attention in most scenarios.
Co-Buchi Barrier Certificates for Discrete-time Dynamical Systems
Venue: arXiv cs.FL 2026
Year: 2026

Barrier certificates provide functional overapproximations for the reachable set of dynamical systems and provide inductive guarantees on the safe evolution of the system. In automata-theoretic verification, a key query is to determine whether the system visits a given predicate over the states finitely often, typically resulting from the complement of the traditional Buchi acceptance condition. This paper proposes a barrier certificate approach to answer such queries by developing a notion of co-Buchi barrier certificates (CBBCs) that generalize classic barrier certificates to ensure that the traces of a system visit a given predicate a fixed number of times. Our notion of CBBC is inspired from bounded synthesis paradigm to LTL realizability, where the LTL specifications are converted to safety automata via universal co-Buchi automata with a bound on final state visitations provided as a hyperparameter. Our application of CBBCs in verification is analogous: we fix a bound and search for a suitable barrier certificate, increasing the bound if no suitable function can be found. We then use these CBBCs to verify our system against properties specified by co-Buchi automata and demonstrate their effectiveness via some case studies.
Context Adaptive Cooperation
Venue: arXiv cs.DC 2026
Year: 2026

As shown by Reliable Broadcast and Consensus, cooperation among a set of independent computing entities (sequential processes) is a central issue in distributed computing. Considering $n$-process asynchronous message-passing systems where some processes can be Byzantine, this paper introduces a new cooperation abstraction denoted Context-Adaptive Cooperation (CAC). While Reliable Broadcast is a one-to-$n$ cooperation abstraction and Consensus is an $n$-to-$n$ cooperation abstraction, CAC is a $d$-to-$n$ cooperation abstraction where the parameter $d$ ($1\leq d\leq n$) depends on the run and remains unknown to the processes. Moreover, the correct processes accept the same set of $\ell$ pairs $\langle v,i\rangle$ ($v$ is the value proposed by $p_i$) from the $d$ proposer processes, where $1 \leq \ell \leq d$ and, as $d$, $\ell$ remains unknown to the processes (except in specific cases). Those $\ell$ values are accepted one at a time in different orders at each process. Furthermore, CAC provides the processes with an imperfect oracle that gives information about the values that they may accept in the future. In a very interesting way, the CAC abstraction is particularly efficient in favorable circumstances. To illustrate its practical use, the paper describes in detail two applications that benefit from the abstraction: a fast consensus implementation under low contention (named Cascading Consensus), and a novel naming problem.
Shadow loss: Memory-linear deep metric learning for efficient training
Venue: arXiv cs.CV 2026
Year: 2026

Deep metric learning objectives (e.g., triplet loss) require storing and comparing high-dimensional embeddings, making the per-batch loss buffer scale as $O(S\cdot D)$, where $S$ is the number of samples in a batch and $D$ is the feature dimension, thus limiting training on memory-constrained hardware. We propose Shadow Loss, a proxy-free, parameter-free objective that measures similarity via scalar projections onto the anchor direction, reducing the loss-specific buffer from $O(S\cdot D)$ to $O(S)$ while preserving the triplet structure. We analyze gradients, provide a Lipschitz continuity bound, and show that Shadow Loss penalizes trivial collapse for stable optimization. Across fine-grained retrieval (CUB-200, CARS196), large-scale product retrieval (Stanford Online Products, In-Shop Clothes), and standard/medical benchmarks (CIFAR-10/100, Tiny-ImageNet, HAM-10K, ODIR-5K), Shadow Loss consistently outperforms recent objectives (Triplet, Soft-Margin Triplet, Angular Triplet, SoftTriple, Multi-Similarity). It also converges in $\approx 1.5\text{-}2\times$ fewer epochs under identical backbones and mining. Furthermore, it improves representation separability as measured by higher silhouette scores. The design is architecture-agnostic and vectorized for efficient implementation. By decoupling discriminative power from embedding dimensionality and reusing batch dot-products, Shadow Loss enables memory-linear training and faster convergence, making deep metric learning practical on both edge and large-scale systems.
A unified framework for learning with nonlinear model classes from arbitrary linear samples
Venue: arXiv cs.LG 2026
Year: 2026

We study the fundamental problem of learning an unknown object from data using a prescribed model class. We introduce a unified framework that accommodates objects in arbitrary Hilbert spaces, general (possibly vector-valued) random linear measurements and general types of nonlinear models. We establish novel learning guarantees for this framework that explicitly relate the required amount of data to structural properties of the model class, yielding near-optimal generalization bounds. A central concept we introduce is the variation of a model class relative to a distribution of sampling operators, which quantifies how the model interacts with the measurement process. Combined with entropy integrals that capture the model's complexity, this forms the foundation of our guarantees. Our framework is sufficiently general to recover and unify various well-known problems, such as matrix sketching, compressed sensing with isotropic measurements and compressed sensing with generative models. In each case, existing results arise as direct corollaries of our theory. For compressed sensing with generative models, we also derive the first guarantees for arbitrary Lipschitz generative maps combined with general linear measurements. Overall, our work provides a unified perspective on learning from general data and introduces novel theoretical guarantees that consolidate, sharpen and extend existing results.
Local certification of geometric graph classes
Venue: arXiv cs.DM 2026
Year: 2026

The goal of local certification is to locally convince the vertices of a graph $G$ that $G$ satisfies a given property. A prover assigns short certificates to the vertices of the graph, then the vertices are allowed to check their certificates and the certificates of their neighbors, and based only on this local view, they must decide whether $G$ satisfies the given property. If the graph indeed satisfies the property, all vertices must accept the instance, and otherwise at least one vertex must reject the instance (for any possible assignment of certificates). The goal is to minimize the size of the certificates. In this paper we study the local certification of geometric and topological graph classes. While it is known that in $n$-vertex graphs, planarity can be certified locally with certificates of size $O(\log n)$, we show that several closely related graph classes require certificates of size $Ω(n)$. This includes penny graphs, unit-distance graphs, (induced) subgraphs of the square grid, 1-planar graphs, and unit-square graphs. These bounds are tight up to a constant factor and give the first known examples of hereditary (and even monotone) graph classes for which the certificates must have linear size. For unit-disk graphs we obtain a lower bound of $Ω(n^{1-δ})$ for any $δ>0$ on the size of the certificates, and an upper bound of $O(n \log n)$. The lower bounds are obtained by proving rigidity properties of the considered graphs, which might be of independent interest.
Joint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks
Venue: arXiv cs.NI 2026
Year: 2026

Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing the volume of transmitted data. In existing implementations, the semantic transceiver is typically pre-trained for a specific task and uniformly adopted by all users. However, due to user heterogeneity in computational and communication capabilities, employing a single, fixed semantic transceiver may degrade the coding efficiency and transmission robustness. To address this issue, we first demonstrate the feasibility of dynamically adjusting the computational and communication overhead of DNN-based semantic transceivers, enabling a more flexible paradigm referred to as Adaptive Semantic Communication (ASC). Building on this concept, we formulate a joint user association and resource allocation problem for ASC in 5G and beyond networks, aiming to maximize overall system utility under energy and latency constraints. However, the problem is very challenging due to the inherent interdependencies among decision variables. To tackle this complexity, we decompose the original problem into three subproblems: (i) ASC scheme selection for each user, (ii) spectrum allocation at each Small-cell Base Station (SBS), and (iii) user association across SBSs. Each subproblem is solved sequentially based on the solutions of the preceding stages. The proposed algorithm efficiently yields near-optimal solutions with polynomial-time complexity. Simulation results demonstrate our approach outperforms existing baselines under various situations.
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect
Venue: arXiv cs.NE 2026
Year: 2026

Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them. However, today's PINNs are often trained for a single physics task and require computationally expensive re-training for each new task, even for tasks from similar physics domains. To address this limitation, this paper proposes a pioneering approach to advance the generalizability of PINNs through the framework of Baldwinian evolution. Drawing inspiration from the neurodevelopment of precocial species that have evolved to learn, predict and react quickly to their environment, we envision PINNs that are pre-wired with connection strengths inducing strong biases towards efficient learning of physics. A novel two-stage stochastic programming formulation coupling evolutionary selection pressure (based on proficiency over a distribution of physics tasks) with lifetime learning (to specialize on a sampled subset of those tasks) is proposed to instantiate the Baldwin effect. The evolved Baldwinian-PINNs demonstrate fast and physics-compliant prediction capabilities across a range of empirically challenging problem instances with more than an order of magnitude improvement in prediction accuracy at a fraction of the computation cost compared to state-of-the-art gradient-based meta-learning methods. For example, when solving the diffusion-reaction equation, a 70x improvement in accuracy was obtained while taking 700x less computational time. This paper thus marks a leap forward in the meta-learning of PINNs as generalizable physics solvers. Sample codes are available at https://github.com/chiuph/Baldwinian-PINN.
MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking
Venue: arXiv cs.NE 2026
Year: 2026

Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past decade. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging inter-task knowledge transfer techniques. Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a need for a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To bridge this gap, we introduce the first open-source benchmarking platform, named MToP, for EMT. MToP incorporates over 50 MTEAs, more than 200 MTO problem cases with real-world applications, and over 20 performance metrics. Based on these, we provide benchmarking recommendations tailored for different MTO scenarios. Moreover, to facilitate comparative analyses between MTEAs and traditional evolutionary algorithms, we adapted over 50 popular single-task evolutionary algorithms to address MTO problems. Notably, we release extensive pre-run experimental data on benchmark suites to enhance reproducibility and reduce computational overhead for researchers. MToP features a user-friendly graphical interface, facilitating results analysis, data export, and schematic visualization. More importantly, MToP is designed with extensibility in mind, allowing users to develop new algorithms and tackle emerging problem domains. The source code of MToP is available at: https://github.com/intLyc/MTO-Platform
Multi-Layered Reasoning from a Single Viewpoint for Learning See-Through Grasping
Venue: arXiv cs.RO 2026
Year: 2026

Sensory substitution enables biological systems to perceive stimuli that are typically perceived by another organ, which is inspirational for physical agents. Multimodal perception of intrinsic and extrinsic interactions is critical in building an intelligent robot that learns. This study presents a Vision-based See-Through Perception (VBSeeThruP) architecture that simultaneously perceives multiple intrinsic and extrinsic modalities from a single visual input, in a markerless manner, all packed into a soft robotic finger using the Soft Polyhedral Network design. It is generally applicable to miniature vision systems placed beneath deformable networks with a see-through design, capturing real-time images of the network's physical interactions induced by contact-based events, overlaid on the visual scene of the external environment, as demonstrated in the ablation study. We present the VBSeeThruP's capability for learning reactive grasping without using external cameras or dedicated force and torque sensors on the fingertips. Using the inpainted scene and the deformation mask, we further demonstrate the multimodal performance of the VBSeeThruP architecture to simultaneously achieve various perceptions, including but not limited to scene inpainting, object detection, depth sensing, scene segmentation, masked deformation tracking, 6D force/torque sensing, and contact event detection, all within a single sensory input from the in-finger vision markerlessly.
A Concentration Bound for TD(0) with Function Approximation
Venue: arXiv cs.LG 2026
Year: 2026

We derive uniform all-time concentration bound of the type 'for all $n \geq n_0$ for some $n_0$' for TD(0) with linear function approximation. We work with online TD learning with samples from a single sample path of the underlying Markov chain. This makes our analysis significantly different from offline TD learning or TD learning with access to independent samples from the stationary distribution of the Markov chain. We treat TD(0) as a contractive stochastic approximation algorithm, with both martingale and Markov noises. Markov noise is handled using the Poisson equation and the lack of almost sure guarantees on boundedness of iterates is handled using the concept of relaxed concentration inequalities.
Continuous Diffusion for Mixed-Type Tabular Data
Venue: arXiv cs.LG 2026
Year: 2026

Score-based generative models, commonly referred to as diffusion models, have proven to be successful at generating text and image data. However, their adaptation to mixed-type tabular data remains underexplored. In this work, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. CDTD is based on a novel combination of score matching and score interpolation to enforce a unified continuous noise distribution for both continuous and categorical features. We explicitly acknowledge the necessity of homogenizing distinct data types by relying on model-specific loss calibration and initialization schemes. To further address the high heterogeneity in mixed-type tabular data, we introduce adaptive feature- or type-specific noise schedules. These ensure balanced generative performance across features and optimize the allocation of model capacity across features and diffusion time. Our experimental results show that CDTD consistently outperforms state-of-the-art benchmark models, captures feature correlations exceptionally well, and that heterogeneity in the noise schedule design boosts sample quality. Replication code is available at https://github.com/muellermarkus/cdtd.
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)
Venue: arXiv cs.LG 2026
Year: 2026

Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data, including local correlations as well as global dependencies. Most existing generative models have failed to effectively learn both the local and global properties of time series data. To address this open problem, we propose a novel time series generative model named 'Time-Transformer AAE', which consists of an adversarial autoencoder (AAE) and a newly designed architecture named 'Time-Transformer' within the decoder. The Time-Transformer first simultaneously learns local and global features in a layer-wise parallel design, combining the abilities of Temporal Convolutional Networks and Transformer in extracting local features and global dependencies respectively. Second, a bidirectional cross attention is proposed to provide complementary guidance across the two branches and achieve proper fusion between local and global features. Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties. Furthermore, we highlight our model's advantage on handling this kind of data via an artificial dataset. Finally, we show our model's ability to address a real-world problem: data augmentation to support learning with small datasets and imbalanced datasets.
Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
Venue: arXiv cs.CV 2026
Year: 2026

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance.
Evaluating Language Model Agency through Negotiations
Venue: arXiv cs.CL 2026
Year: 2026

We introduce an approach to evaluate language model (LM) agency using negotiation games. This approach better reflects real-world use cases and addresses some of the shortcomings of alternative LM benchmarks. Negotiation games enable us to study multi-turn, and cross-model interactions, modulate complexity, and side-step accidental evaluation data leakage. We use our approach to test six widely used and publicly accessible LMs, evaluating performance and alignment in both self-play and cross-play settings. Noteworthy findings include: (i) only closed-source models tested here were able to complete these tasks; (ii) cooperative bargaining games proved to be most challenging to the models; and (iii) even the most powerful models sometimes "lose" to weaker opponents
Manipulating Feature Visualizations with Gradient Slingshots
Venue: arXiv cs.LG 2026
Year: 2026

Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a given feature. Despite its popularity, the trustworthiness of FV explanations has received limited attention. We introduce Gradient Slingshots, a novel method that enables FV manipulation without modifying model architecture or significantly degrading performance. By shaping new trajectories in off-distribution regions of a feature's activation landscape, we coerce the optimization process to converge to a predefined visualization. We evaluate our approach on several DNN architectures, demonstrating its ability to replace faithful FVs with arbitrary targets. These results expose a critical vulnerability: auditors relying solely on FV may accept entirely fabricated explanations. To mitigate this risk, we propose a straightforward defense and quantitatively demonstrate its effectiveness.